Forecasting stock prices changes using long-short term memory neural network with symbolic genetic programming

Abstract This study introduces an augmented Long-Short Term Memory (LSTM) neural network architecture, integrating Symbolic Genetic Programming (SGP), with the objective of forecasting cross-sectional price returns across a comprehensive dataset comprising 4500 listed stocks in the Chinese market ov...

詳細記述

書誌詳細
主要な著者: Qi Li, Norshaliza Kamaruddin, Siti Sophiayati Yuhaniz, Hamdan Amer Ali Al-Jaifi
フォーマット: 論文
言語:English
出版事項: Nature Portfolio 2024-01-01
シリーズ:Scientific Reports
オンライン・アクセス:https://doi.org/10.1038/s41598-023-50783-0